Security Best Practices
Security is paramount when operating API gateways that handle sensitive data and provide access to AI services.
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Implement Zero-Trust Architecture
Never trust requests implicitly. Validate authentication and authorization for every request, even from internal services. Use mutual TLS for service-to-service communication.
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Rotate API Keys Regularly
Implement automated key rotation every 90 days or sooner. Use secret management systems (HashiCorp Vault, AWS Secrets Manager) instead of environment variables or config files.
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Enable Rate Limiting at Multiple Levels
Implement rate limiting per user, per IP, and globally. Protect against both abuse and accidental overload. Use token bucket or sliding window algorithms.
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Audit All Access
Log all authentication attempts, authorization decisions, and administrative actions. Retain logs for compliance requirements (typically 90+ days). Enable audit trails for forensic analysis.
Performance Optimization
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Implement Semantic Caching
Cache responses based on semantic similarity rather than exact matches. This can reduce API costs by 30-60% for similar queries while maintaining response quality.
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Use Connection Pooling
Reuse HTTP connections to upstream providers. Connection pooling reduces latency by 20-40% and prevents resource exhaustion from frequent connection establishment.
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Batch Requests Where Possible
Combine multiple operations into single API calls when providers support batching. This reduces network overhead and improves throughput significantly.
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Enable Compression
Use gzip or brotli compression for API responses. This reduces bandwidth usage by 60-80% for text-heavy responses and improves client-side performance.
Performance Configuration Example
performance:
connection_pool:
max_connections: 100
max_per_host: 20
idle_timeout: 60s
caching:
enabled: true
type: semantic
similarity_threshold: 0.95
ttl: 3600
compression:
enabled: true
algorithms: [brotli, gzip]
min_size: 1024Reliability Patterns
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Implement Circuit Breakers
Automatically fail fast when upstream providers are experiencing issues. Configure appropriate failure thresholds and recovery timeouts to prevent cascade failures.
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Use Multiple Providers
Implement automatic failover between AI providers to ensure availability. Route traffic intelligently based on provider health, cost, and performance.
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Set Aggressive Timeouts
Configure appropriate timeouts for all operations: connection (5s), request (30s), and idle (60s). Prevent resource exhaustion from hanging requests.
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Implement Graceful Degradation
Return cached or fallback responses when upstream services are unavailable. Communicate degraded state to clients via custom headers or response codes.
Operational Excellence
Monitoring Requirements
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Track the Four Golden Signals
Monitor latency, traffic, errors, and saturation. Set up dashboards for real-time visibility and configure alerts for anomaly detection.
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Implement Cost Tracking
Track API costs by user, endpoint, and provider. Set budget alerts to prevent unexpected bills. Provide cost attribution to business units.
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Maintain Runbooks
Document standard operating procedures for common issues. Include escalation paths, contact information, and step-by-step remediation instructions.
Deployment Best Practices
deployment:
strategy: canary
canary:
percentage: 10
duration: 10m
health_check:
endpoint: /health/ready
interval: 10s
timeout: 5s
rollback:
automatic: true
threshold: 5%Cost Optimization Strategies
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Intelligent Model Routing
Route requests to the most cost-effective model that meets quality requirements. Use smaller models for simple queries and larger models for complex tasks.
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Token Optimization
Implement prompt compression and response truncation. Remove unnecessary context and optimize prompts to reduce token usage by 20-40%.
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Request Deduplication
Identify and merge identical or near-identical concurrent requests. This reduces redundant API calls and improves efficiency for popular queries.